8 Use Cases for Clinical Phenotypes Estimating numbers of patients potentially eligible for a proposed trial (study feasibility). Identifying patients for recruitment into prospective trials. Describing patient cohorts for analysis of existing data for comparative effectiveness or health services research. Presenting baseline characteristics or conditions to describe research populations. Presenting primary outcomes to test the trial hypothesis. The implementation of supportive tools for providers that are embedded within EHR systems and clinical workflows.

9 Source:

10 Source: October 1, 2015

11 Use of ICD-9-CM in the US CM clinical modification based on the international ICD to give more detailed codes ICD-9-CM has been used in the US since 1979 (4 years after the international version) for: Classification of morbidity and mortality (mortality reporting changed to ICD-10 since 1999) Reimbursement (since 1983) Analysis of healthcare delivery and cost Epidemiological and clinical research

12 Long road to change ICD-9-CM became more and more out-dated, and there is no way to add new codes because of its rigid code structure 2008 CMS issued NPRM proposing 2011 date 2009 CMS final rule with deadline Oct postponed to Oct Congress passed law to delay ICD-10-CM for at least one year, CMS set new date to Oct 2015

22 General Equivalence Maps Published by CMS and CDC Provide linkages between ICD-9-CM and ICD-10-CM ICD-9-CM volume III (procedures) and ICD-10-PCS Forward (9 to 10) and backward (10 to 9) maps Independent maps, not mirror images Different coverage of ICD-9-CM and ICD-10-CM Partial overlap in the mappings

27 Study methodology Generate ICD-10-CM code sets for each of the 32 ICD-9-CM code sets using each mapping method Generated code sets reviewed by clinical experts for validity Recall, precision and F-score for each mapping method

28 Summary of results Must use both forward and backward GEMs More aggressive methods can identify valid ICD-10- CM targets that are indirectly related to an ICD-9-CM code, but precision is reduced. Choice of method will depend on use case. Works better for well-defined conditions (e.g. colorectal cancer) than vaguely-defined conditions (e.g. chronic pain) Not fully-automated translation manual validation still required

36 Role of SNOMED CT Can help to map from ICD-9-CM to ICD-10-CM 2 maps available: SNOMED CT to ICD-9-CM (IHTSDO) SNOMED CT to ICD-10-CM (NLM) Possible to do sequential mapping from ICD-9-CM to ICD-10-CM through SNOMED CT Use SNOMED CT directly in cohort definitions SNOMED CT codes will become more ubiquitous in EHR More granular concepts fine-tuning of definitions Many quality measure value sets are already defined in SNOMED CT

37 Implications for Pragmatic Trials The same system, organizational, and cultural changes that drive variation of ICD-9 coding will also impact ICD-10 coding There are various tools and approaches to mapping between ICD-9 and ICD-10 There can be variation by organization and system on how these maps are used More problematic in different medical specialties Many convoluted relationships (Boyd et al., 2015)

44 Timing of ICD-10 Transition Relative to Pragmatic Trials Before trial data collection (i.e., study begins after Oct 1, 2015) Can use ICD-10, but cannot re-use past tools i.e., Must build (and validate) new ICD-10 queries based on ICD-9 Historical data in ICD-9 (medical history) might be problematic After trial begins (i.e., study began before Oct 1, 2015) ICD-10-based definitions might change the characteristics of the study population (sampling bias, ascertainment bias) or the depth/accuracy of data collection (measurement bias) In both cases, researchers might have data in both ICD-9 and ICD-10 To compare, need to pick one coding system (ICD-9 or 10) or a reference standard (e.g., SNOMED CT)

47 Reporting recommendations related to Data processing/provenance: Mappings from original values to standardized values Documentation of how original data values were transformed to the target.. format. Documentation should list source values and describe the logic or mappings used to transform original source to required target values.

48 Recommendations Examine phenotype definitions to assess reliance on ICD-9 Consider the phenotype definition as a unit or value set, and compare semantic equivalence of the set Consider different mapping approaches for automatic translation Examine research needs and nature of condition Be prepared to report methods for mapping Be prepared to validate locally Implement data quality assessment recommendations

49 Conclusion ICD 10 will enable researchers to make more targeted data queries and potential have more detailed data for patient risks or outcomes. ICD-10 transition will differentially threaten the research integrity and required resources for various types of studies. Studies where data collection includes the ICD-10 implementation date (October 1, 2015), researchers need to be cognizant of implications of the mapping relationships.

50 Acknowledgments Study presented here is under review with egems: Preparing for the ICD-10-CM Transition: Automated Methods for Translating ICD Codes in Clinical Phenotype Definitions and Quality Measure Value Sets. (Submitted July 2015) Olivier Bodenreider, MD, PhD, National Library of Medicine Michelle Smerek, Duke University Katherine C. Pereira, DNP, Duke University Beverly B. Green, MD, MPH, Group Health Research Institute Ashwin Patkar, MD, Duke University Megan Clowse, MD, Duke University Alan Bauck, Center for Health Research, Kaiser Permanente Northwest This presentation was supported by the National Library of Medicine intramural research program, and the National Institutes of Health (NIH) Common Fund, through a cooperative agreement (U54 AT007748) from the Office of Strategic Coordination within the Office of the NIH Director. The views presented here are solely the responsibility of the authors and do not necessarily represent the official views of the National Institutes of Health. Many thanks to: NIH Collaboratory Phenotype, Data Standards, Data Quality Core Duke Center for Predictive Medicine and Ben Neely, MS

51 DISCUSSION

52 Performance Reproducibility Consistency Research Concerns Identify and eliminate potential bias Goal: at the point of randomization: a) the 2 groups have equal risk of having the outcome of interest; and b) the 2 groups are very well characterized at the point of the start of the trial.

54 ICD-10 Issues by Use Cases Estimating numbers of patients potentially eligible for a proposed trial (study feasibility). Possible Impact: low ; research planning is a one-time activity. In the past it was done with ICD-9, but now can be done with ICD-10 Activities: ICD-9 based phenotypes will need to be converted to ICD-10.

55 ICD-10 Issues by Use Cases Identifying patients for recruitment into prospective trials. ~cohort identification Possible Impact: High. If the study recruitment occurs before and after Oct. 1, 2015, then there is a danger that those recruited after the transition are not the same as those before. Could lead to sampling bias if there are differences (including certainty of disease and severity of conditions) between patients recruited early versus late in study. Activities: ICD-9 Based phenotypes need to be converted to ICD-10 and clinically validated.

56 ICD-10 Issues by Use Cases Describing patient cohorts for analysis of existing data for comparative effectiveness or health services research. Possible Impact: Moderate. If the data analyzed in the study was collected from health systems before and after Oct. 1, 2015, then there might be a systematic bias. Activities: ICD-9 Based phenotypes need to be converted to ICD-10 and clinically validated. Data quality assessment recommendations can be applied.

57 ICD-10 Issues by Use Cases Presenting baseline characteristics or conditions to describe research populations by demographics, clinical features, and co-morbidities for clinical trials. Possible Impact: High. If the study recruitment occurs before and after Oct. 1, 2015, then there is a danger that those recruited after the transition are not the same as those before. Could lead to sampling bias if there are differences (including certainty of disease and severity of conditions) between patients recruited early versus late in study. Activities: ICD-9 Based phenotypes need to be converted to ICD- 10 and clinically validated.

58 ICD-10 Issues by Use Cases Presenting primary outcomes to test the trial hypothesis. The implementation of supportive tools for providers that are embedded in EHR systems and clinical workflows. Possible Impact: High. If the study outcomes are assessed for some patients before and some after Oct. 1, 2015, then there could be differences (including certainty of disease and severity of conditions) between patients assessed early versus late in study. Activities: ICD-9 Based phenotypes need to be converted to ICD-10. Aggressive (iterative) mapping processes appropriate. New ICD-10 groups must be clinically validated. Data quality assessment recommendations can be applied.

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